On Impedance-Pattern Selection for Noise Parameter Measurement
Bibliographic record
Abstract
Several signal-source impedances (admittances) are required to fully characterize the noise behavior of a linear device. This paper expands the theory of choosing admittances required for noise parameter extraction by finding sets that are guaranteed to form systems of linearly independent equations. A minimal set of four admittances is chosen from four linearly independent admittance regions. Most prior methods require a least-squares solution to noise parameters, using a redundant number of admittances. The proposed method employs a direct solution to the noise parameters using the four admittances. The proposed method also allows adapting the four admittances to overcome signal-source admittance-tuner frequency limitations and/or reducing uncertainty in the minimum noise factor extraction provided approximate knowledge of optimal admittance for minimum noise. The measurement and simulation results demonstrated that the adaptable selection criterion extracted noise parameters well within 3σ uncertainties of noise parameters found with patterns previously reported in literature. Measurements and theory in this work demonstrate that, in general, absolute reflection coefficients of source admittances do not need to exceed approximately 0.4 but should also be less than 0.9. The flexibility of selecting the signal-source admittances has immediate advantages in accurately determining noise parameters of conditionally stable amplifiers, low-frequency devices operating beyond the tuner specifications, devices operating at high frequencies where tuner losses are high, minimizing measurement time, and cases where available admittances do not encompass regions required by other methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".